Genetic mapping of complex traits has been successful over the last decade, with over 2,000 regions in the genome associated to disease. Yet, the translation of these findings into a better understanding of disease biology is not straightforward. The true promise of human genetics lies in its ability to explain disease etiology, and the need to translate genetic findings into a better understanding of biological processes is of great relevance to the community. We hypothesized that integrating genetics and protein- protein interaction (PPI) networks would shed light on the relationship among genes associated to complex traits, ultimately to help guide understanding of disease biology. First, we discuss the design, testing and implementation of a novel in silico approach (“DAPPLE”) to rigorously ask whether loci associated to complex traits code for proteins that form significantly connected networks. Using a high-confidence set of publically available physical interactions, we show that loci associated to autoimmune diseases code for proteins that assemble into significantly connected networks and that these networks are predictive of new genetic variants associated to the phenotypes in question. Next, we study variation in the electrocardiographic QT-interval, a heritable phenotype that when prolonged is a risk factor for cardiac arrhythmia and sudden cardiac death. We show that a large proportion of QT-associated loci encode proteins that are members of complexes identified by immunoprecipitations in mouse cardiac tissue of proteins known to be causal of Mendelian long-QT syndrome. For several of the identified proteins, we show they affect cardiac ion channel currents in model organisms. Using replication genotyping in 17,500 individuals, we use the complexes to identify genome-wide significant loci that would have otherwise been missed. Finally, we consider whether PPIs can be used to interpret rare and de novo variation discovered through recent technological advances in exome-sequencing. We report a highly connected network underlying de novo variants discovered in an autism trio exome-sequencing effort, and we design, test and implement a novel statistical framework (“DAPPLE/SEQ”) to analyze rare inherited variants in the context of PPIs in a way that significantly boosts power to detect association.

Genome-wide association studies (GWAS) of common diseases have identified hundreds of genomic regions harboring disease-associated variants. Translating these findings into an improved understanding of human disease requires identifying the causal variants(s) and gene(s) in the implicated regions which, to date, has only been accomplished for a small number of associations. Several factors complicate the identification of mutations playing a causal role in disease. First, GWAS arrays survey only a subset of known variation. The true causal mutation may not have been directly assayed in the GWAS and may be an unknown, novel variant. Moreover, the regions identified by GWAS may contain several genes and many tightly linked variants with equivalent association signals, making it difficult to decipher causal variants from association data alone. Finally, in many cases the variants with strongest association signals map to non-coding regions that we do not yet know how to interpret and where it remains challenging to predict a variants likely phenotypic impact. Here, we present a framework for the genetic and functional study of intergenic regions identified through GWAS and describe application of this framework to chromosome 9p21: a non-coding region with associations to type 2 diabetes (T2D), myocardial infarction (MI), aneurysm, glaucoma, and multiple cancers. First, we compare methods for genetic fine-mapping of GWAS associations, including methods for creating a more comprehensive catalog of variants in implicated regions and methods for capturing these variants in case- control cohorts. Next, we describe an approach for using massively parallel reporter assays (MPRA) to systematically identify regulatory elements and variants across disease-associated regions. On chromosome 9p21, we fine-map the T2D and MI associations and identify, for each disease, a collection of common variants with equivalent association signals. Using MPRA, we identify hundreds of regulatory elements on chromosome 9p21 and multiple variants (including MI- and T2D-associated variants) with evidence for allelic effects on regulatory activity that can serve as a foundation for further study. More generally, the methods presented here have broad potential application to the many intergenic regions identified through GWAS and can help to uncover the mechanisms by which variants in these regions influence human disease.

No description available.

Taking the advantage of the high-throughput Single Nucleotide Polymorphism (SNP) genotyping technology, Genome-Wide Association Studies (GWASs) are regarded holding promise for unravelling complex relationships between genotype and phenotype. GWASs aim to identify genetic variants associated with disease by assaying and analyzing hundreds of thousands of SNPs. Traditional single-locus-based and two-locus-based methods have been standardized and led to many interesting findings. Recently, a substantial number of GWASs indicate that, for most disorders, joint genetic effects (epistatic interaction) across the whole genome are broadly existing in complex traits. At present, identifying high-order epistatic interactions from GWASs is computationally and methodologically challenging. My dissertation research focuses on the problem of searching genome-wide association with considering three frequently encountered scenarios, i.e. one case one control, multi-cases multi-controls, and Linkage Disequilibrium (LD) block structure. For the first scenario, we present a simple and fast method, named DCHE, using dynamic clustering. Also, we design two methods, a Bayesian inference based method and a heuristic method, to detect genome-wide multi-locus epistatic interactions on multiple diseases. For the last scenario, we propose a block-based Bayesian approach to model the LD and conditional disease association simultaneously. Experimental results on both synthetic and real GWAS datasets show that the proposed methods improve the detection accuracy of disease-specific associations and lessen the computational cost compared with current popular methods.

The central goal of human genetics is to understand genetic differences both within and between populations and how these differences contribute to phenotypic variation. Recent advances in genotyping technologies and statistical methodology mean that we can now examine population differences at high genetic resolution, and attempt to find common variants that underlie variation in complex traits in the population. In this thesis, differences in maternal genetic ancestry in Australia were examined and a number of genetic association studies were undertaken in an attempt to map genetic variants that underlie complex traits. Abstract Before presenting the results from the five main genetic analyses, an overview is given of the history of gene-mapping in humans, the challenges this has presented, and the major discoveries from both empirical and theoretical studies that have advanced the field of human genetics to the point where hypothesis-free association testing of common variants with complex traits is now possible. The reasons why mitochondrial DNA has proved so useful in examining the history of populations, and the major findings from the field of mitochondrial population genetics are summarised. In addition, some of the major evidence of a role for mitochondrial variants in complex trait variation is presented. For the first main paper, data from 69 mitochondrial variants that tag the majority of common mitochondrial SNPs in European populations was used to test whether there is evidence for population stratification (i.e. the presence of more than one randomly mating population) in the maternal genetic line of modern Australians. By combining the genetic data with self-reported maternal ancestry data, it was shown that there are significant differences in the patterns of mitochondrial variation between groups of individuals whose maternal ancestors came from different areas of the world. Specifically, it was shown that there are significant differences between groups from different regions of Europe, with those from Eastern Europe showing large differences in SNP and haplogroup frequencies compared to the other groups. A test for assortative mating was performed by comparing whether mates in our sample shared more mitochondrial variants in common when compared to randomly drawn pairs from the population. No evidence of increased sharing was found. The second study involved testing whether common mitochondrial variants are associated with a number of physiological and biochemical traits, the majority of which are risk factors for the metabolic syndrome and type 2 diabetes. Phenotypic and genotypic data was available for just over 2,000 adolescent twins measured at three different timepoints. This is the first known mitochondrial association study to use family data, and a methodology based on a linear model was presented for performing such an association. In spite of having power to detect variants of modest effect, only viii one significant association was found between mt14365 and triacyglycerol levels in twins measured at age 12. This association was not replicated across the other age groups. The third study used the methodology developed for family-based mitochondrial association studies to test for association between mitochondrial variants and a battery of cognitive tests in twins aged 16. A previous study with a small sample size had shown an association between mitochondria and IQ, but this had never been replicated or followed-up. A total of 1,385 individuals from 665 families were included, but no statistically significant associations were found. The most strongly associated SNP was found in a gene in which variants have been shown to influence cognition in mice with a homogeneous nuclear genetic background. For the fourth study, a genome-wide association analysis was carried out of 6 self-reported traits related to the menstrual cycle. Sample sizes ranged from 468 for age at menopause to 5,743 for age at menarche. No SNPs were found to be associated at a genome-wide significant level, however, the results from previous association analyses of age at menarche and age at menopause were replicated. A number of regions for each trait that show modest evidence of association have been identified, and these should be targeted for replication in another sample. In addition, a number of genes that show strong evidence for association with each trait were identified and using a multivariate approach, a SNP in the RNA polymerase III subunit B gene was shown to potentially have a pleiotropic effect on age at menarche and duration of menses. In the final study, a genome-wide association study data for self-reported caffeine consumption and caffeine-related sleep disturbance was performed. A number of loci that potentially influence each trait were identified. The association data was combined with gene expression data from three cell types that had been treated with caffeine. A gene-based test was performed to test whether genes that were found to be consistently up- or down-regulated by caffeine treatment show increased evidence of association. There was no evidence of increased association signals in these genes. A number of the caffeine-regulated genes show strong evidence for overall association and represent good candidate genes for targeted replication in a larger sample. Finally, a synthesis of the main results of each study is presented including potential limitations of this research. This discussion includes a critical assessment of the current findings in both mitochondrial genetics and genome-wide association studies, and potential future directions in the field of gene-mapping in humans.

Thesis (MSc)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: Described as one of the costliest and most debilitating disorders, schizophrenia has proven to be among the greatest challenges for medical researchers. The disorder poses difficulties on all levels: from genotype to phenotype. Even though it is known that there is a substantial genetic contribution to schizophrenia susceptibility (~80%), it is unknown whether this is due to common variants, rare variants, epigenetic factors, polymorphisms in regulatory regions of the genome or a combination of all these factors. Over the past few decades, many approaches have been employed to elucidate the genetic architecture of schizophrenia, with the latest and most promising being genome wide association studies (GWAS). However, nearly a decade after the first GWAS, the limitations are increasingly being recognised and new avenues need to be explored. Studies have recently started to focus on the analysis of non-coding regions of the genome since these regions harbour the majority of variants identified in GWAS thus far. This study aimed to use recently developed programs that utilize data from large scale studies such as previous GWAS, the Encyclopaedia of DNA Elements (ENCODE), 1000 Genomes, HapMap and Functional Annotation of the Mammalian Genome (FANTOM) to establish a simple, yet effective bioinformatics pipeline for the identification and assessment of variants in regulatory regions. Using the established workflow, 149 single nucleotide polymorphisms (SNPs) in regulatory regions were implicated in schizophrenia susceptibility, with the most significant SNP being rs200981. Pathway and network analysis using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and GeneMANIA respectively indicated that the most frequently affected genes were involved in immune responses or neurodevelopmental processes, which support previous findings. Yet, novel findings of this study implicated processes crucial for DNA packaging (from DNA level to chromatin level). The second part of the study used restriction fragment length polymorphism analysis of polymerase chain reaction-amplified fragments (PCR-RFLP) to genotype ten of the most significant SNPs (identified by bioinformatic analyses in the first part of the study) in a South African Xhosa cohort of 100 cases and 100 controls, while bi-directional Sanger sequencing was used to confirm the presence of these SNPs. Statistical analyses revealed two haplotypes of regulatory variants, rs200483-rs200485-rs2517611 (p = 0.0385; OR = 1.71; 95% CI = 1.01-2.91) and rs200981-rs2517611-rs3129701 (p = 0.041; OR = 0.51; 95% CI = 0.27-0.98) associated with schizophrenia susceptibility. Bioinformatic analysis indicated that these haplotypes affect DNA packaging, which supported the findings of the first part of the study and could implicate epigenetic processes. The findings of this study support the importance of regulatory variants in schizophrenia susceptibility. This study also showed the importance of combining GWAS data with additional analyses in order to better understand complex diseases. It is hoped that these findings could fuel future research, specifically in genetically unique populations. / AFRIKAANSE OPSOMMING: Skisofrenie kan beskryf word as een van die duurste en mees ernstige siektes en bly steeds een van die grootste uitdagings vir mediese navorsers. Hierdie versteuring behels probleme op alle vlakke: van genotipe tot fenotipe. Alhoewel dit bekend is dat daar 'n aansienlike genetiese bydrae tot skisofrenie vatbaarheid is (~ 80%), is dit onbekend of dit is as gevolg van algemene variasies, skaars variasies, epigenetiese faktore, variasies in regulerende gebiede van die genoom of 'n kombinasie van al hierdie faktore. Oor die afgelope paar dekades is verskeie benaderings gebruik om die genetiese samestelling van skisofrenie te bestudeer, met die nuutste en mees belowende synde genoom-wye assosiasie studies (GWAS). Byna 'n dekade na die eerste GWAS, word die beperkinge egter toenemend erken en nuwe navorsingstrategieë moet gebruik word. Studies het onlangs begin om meer te fokus op die analise van nie-koderende areas van die genoom aangesien hierdie areas die meerderheid van die variasies behels wat tot dusver in GWAS geïdentifiseer is. Hierdie studie het gepoog om onlangs ontwikkelde programme, wat gebruik maak van die data van grootskaalse studies soos vorige GWAS, die “Encyclopaedia of DNA Elements” (ENCODE), “1000 Genomes”, “HapMap” en “Functional Annotation of the Mammalian Genome” (FANTOM), te implementeer om sodoende 'n eenvoudige, maar doeltreffende bioinformatika pyplyn vir die identifisering en evaluering van variante in regulerende gebiede, te vestig. Deur die gebruik van die gevestigde bioinformatika pyplyn, is 149 enkel nukleotied polimorfismes (SNPs) in regulerende gebiede in skisofrenie vatbaarheid betrek, met rs200981 wat die mees betekenisvol was. Pad- en netwerk-analise met die onderskeidelike hulp van die “Database for Annotation, Visualization and Integrated Discovery” (DAVID) en “GeneMANIA”, het aangedui dat die gene wat die meeste geaffekteer was, betrokke is by immuunreaksies en neuro-ontwikkeling. Hierdie bevindinge ondersteun vorige studies. Tog het nuwe bevindinge van hierdie studie prosesse geïmpliseer wat uiters noodsaaklik is vir DNS verpakking (van DNS- tot chromatien-vlak). Die tweede deel van die studie het restriksie fragment lengte polimorfisme analise van polimerase ketting reaksie geamplifiseerde fragmente (PKR-RFLP) gebruik om tien van die belangrikste SNPs (wat geïdentifiseer is deur bioinformatiese ontledings in die eerste deel van die studie) in `n Suid-Afrikaanse Xhosa studiegroep van 100 skisofrenie gevalle en 100 kontroles te genotipeer, terwyl tweerigting Sanger volgordebepaling gebruik is om die teenwoordigheid van hierdie SNPs te bevestig. Statistiese analise het aangedui dat twee / National Research Foundation (DAAD-NRF)

2’ -5’ –oligoadenylate synthetase 1 (OAS1) is an antiviral enzyme that in the presence of double-stranded RNA structures, such as viral genomes or single-stranded RNA transcripts with significant double-stranded character, converts ATP to a series of 2’ -5’ –oligoadenylates (2-5A). 2-5A promotes dimerization of latent ribonuclease (RNaseL) to form catalytically active RNaseL, a candidate hereditary prostate cancer (PCa) gene. RNaseL is anti-proliferative and promotes senescence and apoptosis in PCa cells. Genotyping analysis was completed on over 600 genomic DNA samples from African-American and Caucasian, normal and PCa subjects. Genotyping was performed to screen the following SNPs in the last exon of OAS1 (rs10774671, rs1131476, rs1051042 and rs2660) to determine splicing and linkage disequilibrium (LD) or LD decay in relation to PCa. The rs10774671 GG and AA genotypes generate isoform 1 (p46) and isoform 3 (p48), respectively and were distributed equally in the healthy population. However, in cases, the AA genotype (p46) was significantly associated with PCa risk (OR: 1.80, P-value: < 0.0001). The genotypic frequencies of rs1131476, rs1051042 and rs2660 demonstrated significant LD but showed no association to PCa risk. We also identified protective (AACA, OR =0.06612, P < 0.001) and risk (GACA, OR= 2.31, p Additionally, we utilized two genome-wide association studies analyzing OAS1 and variants found on chromosome 12 to determine their relationship with PCa susceptibility for meta-analysis: This was done to elucidate the role of OAS1 SNPs and chromosome 12 variants in a larger population cohort with PCa susceptibility for a greater understanding of gene to gene interactions. The genome wide association studies used were, the Geneva Multiethnic Genome-wide Scan of Prostate Cancer (MEC), containing 2,841 African-American samples (1,343 cases and 1,498 controls) and 1,660 Japanese/Latino samples (834 cases and 826 controls), as well as Cancer Genetic Markers of Susceptibility (CGEMS) Prostate Cancer-Primary Scan (Stage 1) - PLCO which contains 2,841 samples of European ancestry (1,172 cases and 1,157 controls). We used PLINK, a whole genome association analysis toolset, to extract data on SNPs in association with PCa.

Recent advances in high-throughput, genomic sequencing allow unprecedented insight into the evolution of biodiversity. Chapter 1 of this thesis is a phylogenetic study of 1,145 sequenced loci, isolated using a novel high-throughput sequence capture methodology to address the phylogenetic position of turtles within tetrapods. The results reported here unambiguously place turtles as sister to archosaurs and resolve this long-standing question. Chapter 2 investigates the genetic basis of colorful pigmentation in the Green anole (Anolis carolinensis) by sequencing complete transcriptomes from the green dorsal, white ventral and pink dewlap skin. Anoles comprise an adaptive radiation of more than 400 species and color plays a central role in their ecology and evolution, but little is known about the genetic basis of colorful pigmentation in any vertebrate. This study identified 1,719 differentially expressed genes among the three differently colored tissues. Twenty-three of these genes are involved in melanin, pteridine, and carotenoid pigmentation pathways that contribute to the coloration of anole skin. Identifying candidate genes for colorful pigmentation is a significant advance that opens the field for comparative analysis in other taxa. To determine if the genes identified in Chapter 2 are involved in population divergence and speciation, Chapter 3 investigates the complete genomes of twenty individuals from two closely related subspecies of Anolis marmoratus. While the two subspecies differ markedly in pigmentation, this study found few genetic differences between populations except in five regions of the genome, which together contained 447 genes. Of these genes, only two, melanophilin (mlph) and 'cluster of differentiation 36' (cd36), are associated with pigmentation. The intersection of the genes identified in Chapter 2 and Chapter 3 includes both cd36 and mlph, suggesting that both are involved in divergence of coloration. Cd36 is of particular interest because it regulates the uptake of carotenoid pigments and is an important candidate gene contributing to carotenoid pigmentation. Together, this research demonstrates the power of genomic approaches to address fundamental questions in systematics, micro-evolution, and speciation. The findings bolster the emerging field of phylogenomics and broadly impact future research into the genetic basis of coloration in vertebrates.

Submitted by Baltasar Fernandes Garcia Neto null (baltasar.fgn@gmail.com) on 2018-03-09T19:05:14Z No. of bitstreams: 1 Diss_Balt_Final.pdf: 637437 bytes, checksum: 99a5603df788f9d4cb2c007a3e8180fd (MD5) / Approved for entry into archive by Alexandra Maria Donadon Lusser Segali null (alexmar@fcav.unesp.br) on 2018-03-12T18:40:44Z (GMT) No. of bitstreams: 1 garcianeto_bf_me_jabo.pdf: 637437 bytes, checksum: 99a5603df788f9d4cb2c007a3e8180fd (MD5) / Made available in DSpace on 2018-03-12T18:40:44Z (GMT). No. of bitstreams: 1 garcianeto_bf_me_jabo.pdf: 637437 bytes, checksum: 99a5603df788f9d4cb2c007a3e8180fd (MD5) Previous issue date: 2018-02-27 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / A complexidade das características que podem apresentar diferentes estruturas de ação gênica como, por exemplo, poligênicas ou afetadas por genes de efeito maior, aliado a diferentes herdabilidades, entre outros fatores, tornam a detecção de QTLs desafiadora. Diversos métodos têm sido empregados com o intuito de realizar estudos de associação ampla do genoma (GWAS), objetivando o mapeamento de QTL. A metodologia weighted single-step GBLUP (wssGBLUP), por exemplo, é uma alternativa para a realização de GWAS, que permite o uso simultâneo de informações genotípicas, de pedigree e fenotípicas, mesmo de animais não genotipados. Métodos Bayesianos também são utilizados para a realização de GWAS, partindo da premissa básica de que a variância observada pode variar em cada locus em uma distribuição a priori específica. O objetivo do presente estudo foi avaliar, por meio de simulações, quais métodos, dentre os avaliados, mais auxiliaria na identificação de QTLs para características poligênicas e afetadas por genes de efeito maior, apresentando diferentes herdabilidades. Utilizamos os métodos: wssGBLUP, com a inclusão ou não de informação adicional fenotípica de animais não genotipados e dois distintos ponderadores para os marcadores, onde w1 representou a mesma ponderação (w1=1) e w2 a ponderação calculada de acordo com o processo de iteração anterior (w1) ; Bayes C, assumindo dois valores para π (π=0.99 and π=0.999), onde π é a proporção de SNPs não incluída no modelo, além do LASSO Bayesiano. Os resultados mostraram que para cenários poligênicos o poder de detecção é menor e o uso adicional de fenótipos de animais não genotipados pode ajudar na detecção, ainda que com pouca intensidade. Para cenários com característica sob efeito maior, houve maior poder na detecção de QTL pelos diferentes métodos em comparação aos cenários poligênicos com destaque para a leve vantagem do método Bayes C. A inclusão de informação fenotípica adicional, entretanto, causou viés nas estimativas e atrapalhou o desempenho do wssGBLUP na presença de QTL com efeito maior. O aumento da v herdabilidade para ambas as estruturas melhorou o desempenho dos métodos e o poder de mapeamento. O método mais adequado para a detecção de QTL depende da estrutura genética e da herdabilidade da característica, não existindo um método que seja superior para todos os cenários. / The complexity of the traits that can present different genetic structures, such as polygenic or affected by genes of major effect, in addition to different heritabilities, among other factors, make the detection of QTLs challenging. Several methods have been employed with the purpose of performing genome wide association studies (GWAS), aiming the mapping of QTL. The single-step weighted GBLUP (wssGBLUP) method, for example, is an alternative to GWAS, which allows the simultaneous use of genotypic, pedigree and phenotypic information, even from non-genotyped animals. Bayesian methods are also used to perform GWAS, starting from the basic premise that the observed variance can vary at each locus with a specific priori distribution. The objective of the present study was to evaluate, through simulation, which methods, among the evaluated ones, more assist in the identification of QTLs for polygenic and major gene affected traits, presenting different heritabilities. We used the following methods: wssGBLUP, with or without additional phenotypic information from non-genotyped animals and two different weights for markers, where w1 represented the same weight (w1=1) and w2 the weight calculated according to the previous iteration process (w1); Bayes C, assuming two values for π (π = 0.99 and π = 0.999), where π is the proportion of SNPs not included in the model, and Bayesian LASSO. The results showed that for polygenic scenarios the detection power is lower and the additional use of phenotypes from non-genotyped animals may help in the detection, yet with low intensity. For scenarios with major effect, there was greater power in the detection of QTL by all different methods with slighter superior performance for the Bayes C method. However, the inclusion of additional phenotypic information caused bias in the estimates and harmed the performance of the wssGBLUP in the presence of major QTL. The increase in heritability for both structures improved the performance of the methods and the power of mapping. The most suitable method for the iii detection of QTL is dependent on the genetic structure and the heritability of the trait, and there is not a superior method for all scenarios.

Submitted by GUILHERME LUÍS PEREIRA null (guipicoia@hotmail.com) on 2017-05-22T14:29:57Z No. of bitstreams: 1 Tese_Guilherme_Luis_Pereira.pdf: 2598868 bytes, checksum: aeb7dc177e4d5e4c0f7a8c7a59a11697 (MD5) / Approved for entry into archive by Luiz Galeffi (luizgaleffi@gmail.com) on 2017-05-23T17:26:30Z (GMT) No. of bitstreams: 1 pereira_gl_dr_jabo.pdf: 2598868 bytes, checksum: aeb7dc177e4d5e4c0f7a8c7a59a11697 (MD5) / Made available in DSpace on 2017-05-23T17:26:30Z (GMT). No. of bitstreams: 1 pereira_gl_dr_jabo.pdf: 2598868 bytes, checksum: aeb7dc177e4d5e4c0f7a8c7a59a11697 (MD5) Previous issue date: 2017-04-28 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Dentre os equinos selecionados para velocidade, a linhagem de corrida da raça Quarto de Milha se destaca pelo alto desempenho em provas de curtas distâncias, sendo considerados os mais velozes do mundo. Apesar de, no Brasil, o efetivo de animais ser relativamente menor na linhagem de corrida do que nas demais, sua importância econômica é substancial. Tendo em vista o interesse econômico e científico relacionado a esta característica atlética, poucos esforços têm sido realizados para a maior compreensão de seus mecanismos genéticos e fisiológicos. Este trabalho teve como objetivos: 1) realizar a imputação de genótipos em duas vias entre indivíduos de uma amostra populacional relativamente pequena de cavalos de corrida da raça Quarto de Milha genotipados com painéis de 54k ou de 65k, bem como avaliar a acurácia de imputação por meio de simulações; 2) realizar estudo de associação ampla do genoma (GWAS) em cavalos da linhagem de corrida da raça Quarto de Milha por meio da utilização de chips equinos de genotipagem de SNPs, visando a prospecção de regiões cromossômicas, genes e polimorfismos relacionados ao desempenho; 3) analisar exomas de equinos de corrida da raça Quarto de Milha contrastantes para Índice de Velocidade máximo (IV max) em regiões previamente associadas à característica por meio de GWAS, visando a prospecção de polimorfismos gênicos causais, ligados ou em forte desequilíbrio de ligação com o desempenho em corridas. A imputação foi realizada utilizando 116 cavalos genotipados com o arranjo de SNPs de 54k e 233 genotipados com arranjo de 65k. Nas simulações foram escolhidas amostras aleatórias para constituírem as populações imputadas e referências em dois cenários. O cenário A simulou a imputação genótipos na primeira via (65k para 54k) e o cenário B na segunda (54k para 65k). No cenário A foram considerados 113 indivíduos para a população referência e 236 para a imputada, dos quais 116 e 120 foram genotipados com os arranjos de 54k e 65k, respectivamente. No cenário B foram considerados 50 indivíduos para a população referência e 299 para a imputada, dos quais 66 e 233 foram genotipados com os arranjos de 54k e 65k, respectivamente. Com isso, após o controle de qualidade, os painéis de 54k e de 65k contaram com 7.048 e 16.940 marcadores exclusivos, respectivamente. As médias de taxa de concordância para os cenários A e B foram 0,9815 e 0,9751 e para r2 alélico foram 0,9791 e 0,9740, respectivamente. O GWAS foi realizado com base no método single step GBLUP por meio de duas abordagens: ssGWAS1, em que somente efeitos de SNPs são reestimados a cada iteração, e ssGWAS2, em que a cada iteração são reestimados efeitos de SNPs a partir de valores genético genômico (GEBVs) reestimados. Vinte e uma regiões foram encontradas explicando mais que 1% da variância genética total (gVar) da característica índice de velocidade máximo (IV max) para ssGWAS1 e doze parassGWAS2. No total mais de 40% da gVar foi explicada por estas regiões para ssGWAS1 e cerca de 30% para ssGWAS2. Entre os cromossomos que explicaram mais de 1% da variância genética, cinco foram comuns aos dois métodos (ECA 3, 10, 15, 22, 25). Foram identificados 108 genes na primeira abordagem e 59 na segunda. A partir de informações de GEBVs de cada cavalo foram formados dois grupos de animais contrastantes para desempenho em corridas (20 animais de IV max superior e 20 IV max inferior), para ser sequenciados. Foram observadas leituras de boa qualidade para toda extensão das reads sequenciadas (até 100pb) e cobertura média de 43x. Foram identificadas 1.203 variantes (1.105 SNPs e 93 InDels) em 33 regiões de interesse obtidas, anteriormente, por meio de estudo de GWAS, das quais 61,3% não estavam registradas/depositadas no banco de dados de variantes equino. Do total de polimorfismos, 29 (24 SNPs e 5 InDels) foram considerados de importância com base em três abordagens distintas e independentes: escores SIFT classificado como deletério (<0,05), grau de impacto na região consenso de cada polimorfismo, e frequências alélicas diferentes, identificadas pelo teste de Fisher (p< 0,01), entre os grupos de cavalos contrastantes para IV max. Com isso, oito genes descritos como candidatos em trabalhos anteriores (ABCG5, COL11A1, GEN1, SOCS3, MICAL1, SPTBN1, EPB41L3 e SHQ1), e oito genes candidatos novos (AKNA, ARMC2, FKBP15, LHX1, NOL10, TMEM192, ZFP37, FIG4 e HNRNPU) foram relacionados ao desempenho em corridas de cavalos da raça Quarto de Milha. Assim, os resultados obtidos neste trabalho mostraram que o desempenho em corridas na raça Quarto de Milha, dado pelo IV max, é característica quantitativa e que não há ocorrência de major genes. / Among horses selected for speed, the racing line of Quarter Horses is characterized by high performance in sprint races, with these animals being considered the fastest horses in the world. Although in Brazil the effective number of animals in the racing line is relatively smaller compared to the other lines, its economic importance is substantial. Despite economic and scientific interest in this athletic trait, few efforts have been made to better understand the genetic and physiological mechanisms underlying this trait. The objectives of this study were: 1) to perform two-step genotype imputation between individuals in a relatively small population sample of racing Quarter Horses genotyped with the 54k or 65k panel, and to evaluate the accuracy of imputation through simulations; 2) to perform genome-wide association studies (GWAS) in Quarter Horses of the racing line using equine SNP genotyping chips for prospecting chromosome regions, genes and polymorphisms related to performance; 3) analyze exomes and UTRs in regions previously associated with this trait by GWAS in Quarter Horse racehorses with contrasting maximum speed index (SImax), prospecting causal gene polymorphisms that are related to or are in strong linkage disequilibrium with racing performance. Genotypes were imputed using 116 horses genotyped with the 54k SNP array and 233 animals genotyped with the 65k array. For the simulations, random samples were chosen to compose the imputed and reference populations in two scenarios. Scenario A simulated the genotype imputation in the first step (from 65k to 54k) and scenario B in the second step (from 54k to 65k). Thus, after quality control, the 54k and 65k panels contained 7,048 and 16,940 exclusive markers, respectively. The mean concordance rate was 0.9815 and 0.9751 for scenarios A and B, and the mean allelic r2 was 0.9791 and 0.974, respectively. After imputation was performed by the single-step GBLUP method using two approaches: ssGWAS1 in which only SNP effects are recalculated at each iteration, and ssGWAS2 in which SNP effects are recalculated from genomic estimated breeding values (GEBVs) at each iteration. Twenty-one regions that explained more than 1% of the total genetic variance (gVar) in the maximum speed index were identified by ssGWAS1 and 12 by ssGWAS2. More than 40% of gVar was explained by these regions in ssGWAS1 and about 30% in ssGWAS2. Among chromosomes that explained more than 1% of genetic variance, five were common to both methods (ECA 3, 10, 15, 22, 25). A total of 108 genes were identified with the first approach and 59 with the second approach. To exome sequencing, GEBVs were used for the formation of two groups of animals with contrasting racing performance (20 animals with superior SI max and 20 with inferior SI max). Good quality data were obtained throughout the reads sequenced, with an average coverage of 43x. A total of 1,203 variants (1,105 SNPs and 93 InDels) were identified in 33 regions of interest obtained previously by GWAS; of these, 61.3% were not registered/deposited in the horse genomic variant database. Twenty-nine of the polymorphisms (24 SNPs and 5 InDels) were considered to be important based on three different and independent approaches: SIFT scores classified as deleterious (<0.05), degree of impact on the consensus region of each polymorphism, and different allele frequencies identified by Fisher’s exact test (p< 0.01) between the groups of horses with contrasting SImax. Thus, eight genes described as functional and positional candidates in previous studies (ABCG5, COL11A1, GEN1, SOCS3, MICAL1, SPTBN1, EPB41L3, and SHQ1) and eight new candidate genes (AKNA, ARMC2, FKBP15, LHX1, NOL10, TMEM192, ZFP37, FIG4, and HNRNPU), some of them with known function, were related to racing performance in Quarter Horses. Taken together, the present results show that the racing performance of Quarter Horses, given by the maximum speed index, is a quantitative trait and that no major genes exist.